
#import dependencies
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import helpers
import matplotlib.dates as mdates
import plotly.express as px
import plotly.graph_objects as go
import re
import warnings
warnings.filterwarnings('ignore')
sb.set_style("darkgrid")
%matplotlib inline


# load data
df = pd.read_csv("cleaned_data/reddit_cleaned_2018.csv")
df_merged = pd.read_csv("cleaned_data/reddit_merged_2018.csv")

df_users = pd.read_csv("cleaned_data/users_cleaned.csv")


# convert to datetime
df.created_at = pd.to_datetime(df.created_at)
df_merged.created_at = pd.to_datetime(df_merged.created_at)
df_merged.user_created_at = pd.to_datetime(df_merged.user_created_at)
df_users.user_created_at = pd.to_datetime(df_users.user_created_at)


px.bar(data_frame=df['submission_comment'].value_counts().to_frame().reset_index(),
       x="index", y="submission_comment", color='submission_comment').update_layout(title='Comment or Submission',
                   xaxis_title='contribution category',
                   yaxis_title='number of contributions').update_traces(marker_color='#5296dd')


df.author.value_counts().to_frame().head(10).reset_index()


fig = px.bar(df.author.value_counts().to_frame().head(10).reset_index(), x="author", y="index",
             height=500,
             title='Most commented user in 2018').update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='number of comments',
                   yaxis_title='user name').update_traces(marker_color='#5296dd')

fig.update_yaxes(autorange="reversed")


df.author.value_counts().to_frame().head(10).reset_index()


df_auto_moderator = df.query(" author == 'AutoModerator' ").reset_index(drop=True)
print(df_auto_moderator.shape)
df_auto_moderator.head(1)

(50, 17)


df_auto_moderator.subreddit.value_counts().head(10)

r/DC_Cinematic        12
r/JerkOffToCelebs      6
r/AskReddit            4
r/worldnews            4
r/Celebs               3
r/youtube              2
r/unpopularopinion     2
r/videos               2
r/politics             1
r/photoshopbattles     1
Name: subreddit, dtype: int64


df_auto_moderator['permalink'].iloc[26]

'/r/worldnews/comments/9ymw4g/the_real_reason_amber_heard_hesitated_to_take_her/ea2jaoy/'


df_auto_moderator.text.value_counts().head();


df_night_chicken = df.query(" author == 'Night_Chicken' ").reset_index(drop=True)
print(df_night_chicken.shape)
df_night_chicken.head()

(135, 17)


df_night_chicken['permalink'].iloc[0]

'/r/Celebs/comments/7pysyp/amber_heard/dslw4u6/'


df_night_chicken.subreddit.value_counts().head(10)

r/Celebs    135
Name: subreddit, dtype: int64


df_night_chicken.text.value_counts().head()

What?  What did she hear?                                                                                           121
What? What did she hear?                                                                                              4
I want to know as well.                                                                                               1
What?  Thank you!                                                                                                     1
Excellent!  Not as exciting as the crunching and jostling sounds in Elon's full pockets which escaped her grasp.      1
Name: text, dtype: int64


df_emily = df.query(" author == 'emilyguy' ").reset_index(drop=True)
print(df_emily.shape)
df_emily.head()

(38, 17)


df_emily.submission_comment.value_counts()

submission    34
comment        4
Name: submission_comment, dtype: int64


df_emily['permalink'].iloc[0]

'/r/gentlemanboners/comments/82wa5c/amber_heard/'


df_emily.subreddit.value_counts().head(10)

r/gentlemanboners      19
r/Celebs                9
r/celebsnaked           4
r/celebnsfw             3
r/WatchItForThePlot     2
r/CaraDelevingne        1
Name: subreddit, dtype: int64


df_emily.text.value_counts().head(10)

Amber Heard                        31
The Informers (2008)                2
with Amber Heard                    1
The Informers                       1
The Playboy Club (2011)             1
Amber Heard and Jessica Alba        1
Amber Heard has an amazing body     1
Name: text, dtype: int64


df_users[df_users.user_name == 'ccrraapp']


df_crap = df.query(" author == 'ccrraapp' ").reset_index(drop=True)
print(df_crap.shape)
df_crap.head()

(34, 17)


df_crap.subreddit.value_counts().head(10)

r/geekboners         19
r/DCEUboners         14
r/gentlemanboners     1
Name: subreddit, dtype: int64


df_crap.submission_comment.value_counts()

submission    27
comment        7
Name: submission_comment, dtype: int64


df_crap.text.value_counts().head(10)

[Aquaman] Amber Heard                                                                                                                                    16
Amber Heard                                                                                                                                              10
Idk what to tell you but she slimmed down a bit and is more athletic shaped since she started for Aquaman. Maybe thats why you don't see the old her.     1
Did she lose a lot of weight for this role?  To fit in that suit?                                                                                         1
Oh wow!                                                                                                                                                   1
Yes but she looks slim than before.                                                                                                                       1
Her dress is so perfectly wrapping her.                                                                                                                   1
Nicole Kidman looks stunning. At this point one has to wonder is she even ageing?                                                                         1
She is. Here is [another one](https://i.imgur.com/QlmM5RS.jpg)\n\n                                                                                        1
[Warcraft] Paula Patton [Aquaman] Amber Heard [Sin City] Jessica Alba                                                                                     1
Name: text, dtype: int64


df_crap_contributions = df_crap.groupby(df_crap.created_at.dt.date).size().reset_index(name='n_contributions')
df_crap_contributions.sort_values('n_contributions', ascending=False).head(10)


# check the date this account was creted
df_users[df_users.user_name == 'AutoNewspaperAdmin']


df_auto = df.query(" author == 'AutoNewspaperAdmin' ").reset_index(drop=True)
print(df_auto.shape)
df_auto.head()

(33, 17)


df_auto.subreddit.value_counts().head(10)

r/AutoNewspaper    33
Name: subreddit, dtype: int64


df_auto.text.value_counts().head(10)

[Entertainment] - Amber Heard says she is happy to have moved on with her life | ABC                                       2
[Entertainment] - Amber Heard: Time's Up movement 'has made incredible gains' | USA Today                                  1
[Entertainment] - Amber Heard says meeting Syria refugees left indelible mark | Miami Herald                               1
[Entertainment] - WATCH: Amber Heard dishes on Jason Momoa's pranks on 'Aquaman' set | ABC                                 1
[Entertainment] - Amber Heard: Style Diary | USA Today                                                                     1
[Entertainment] - Janelle Monae, Hillary Clinton, Amber Heard hit the 2018 Glamour Women of the Year Awards | USA Today    1
[Video] - Amber Heard talks superhero roles for women | FOX                                                                1
[Entertainment] - A couture swim cap? Amber Heard rocks an eye-popping headpiece at 'Aquaman' premiere | USA Today         1
[Lifestyle] - Scene City: Bill Clinton, Tony Blair and Amber Heard Made Calls for Charity | NY Times                       1
[Entertainment] - Amber Heard says women face 'skepticism, hostility' when speaking out about abuse | USA Today            1
Name: text, dtype: int64


df_auto = df_auto.groupby(df_auto.created_at.dt.date).size().reset_index(name='n_contributions')
df_auto


df_rad = df.query(" author == 'Rednaxela117' ").reset_index(drop=True)
print(df_rad.shape)
df_rad.head()

(33, 17)


df_rad.subreddit.value_counts().head(10)

r/Celebs               11
r/SexyWomanOfTheDay    10
r/gentlemanboners       8
r/PrettyGirls           2
r/goddesses             1
r/celebritylegs         1
Name: subreddit, dtype: int64


df_rad['permalink'].iloc[5]

'/r/SexyWomanOfTheDay/comments/7zgozg/amber_heard_legs_for_days/'


df_rad.text.value_counts().head(10)

Amber Heard                          18
Crazy, not dumb.                      1
Don't we all haha                     1
Amber Heard: lounging around          1
Amber Heard: easy on the eyes         1
She's as hot as she is crazy haha     1
Red heads, you gotta love them.       1
Gorgeous with red hair in Aquaman     1
Amber Heard: as Mera                  1
Amber Heard: business casual          1
Name: text, dtype: int64


df_rad_contributions = df_rad.groupby(df_rad.created_at.dt.date).size().reset_index(name='n_contributions')
df_rad_contributions.sort_values('n_contributions', ascending=False).head(10)


df.author.value_counts().nlargest(n=25)

-banned-              1666
Night_Chicken          135
AutoModerator           50
emilyguy                38
AutoNewsAdmin           35
ccrraapp                34
Rednaxela117            33
AutoNewspaperAdmin      33
ZorakLocust             27
tenchineuro             22
RuleIV                  22
jeff98379               21
vonmark955              20
InfiniTitans            20
Chronos2016             20
bundt_trundler          18
ZadocPaet               18
nobodycares65           18
Queen1110               17
MightUlt-7              16
Count_Fapula1           16
FlexOutlaw              15
sagar7854               15
NaveHarder              14
AngelaStettner69        13
Name: author, dtype: int64


check_list = df.author.value_counts().nlargest(n=25).index.tolist()[1:]
check_list

['Night_Chicken',
 'AutoModerator',
 'emilyguy',
 'AutoNewsAdmin',
 'ccrraapp',
 'Rednaxela117',
 'AutoNewspaperAdmin',
 'ZorakLocust',
 'tenchineuro',
 'RuleIV',
 'jeff98379',
 'vonmark955',
 'InfiniTitans',
 'Chronos2016',
 'bundt_trundler',
 'ZadocPaet',
 'nobodycares65',
 'Queen1110',
 'MightUlt-7',
 'Count_Fapula1',
 'FlexOutlaw',
 'sagar7854',
 'NaveHarder',
 'AngelaStettner69']


# get a data frame with the most negative-comments users
df_check = df_users[df_users['user_name'].isin(check_list)]
print(df_check.shape)
df_check.head(2)

(24, 10)


df_check['user_name'].nunique()

24


def get_stats(df):
    for col in df.columns:
        if col not in ['user_name', 'user_created_at']:
            if col not in ['link_karma', 'comment_karma']:
                print('The value counts of the users with the most contributions: ' + col)
                print(df_check[col].value_counts())
                print('\n')
                
            else:
                print("The min of {}".format(col), round(df_check[col].min(),2))
                print('\n')
                print("The max of {}".format(col), round(df_check[col].max(),2))
                print('\n')
                print("The mean of {}".format(col), round(df_check[col].mean(),2))
                print('\n')
                print("The median of {}".format(col), round(df_check[col].mean(),2))
                print('\n')


get_stats(df_check)

The value counts of the users with the most contributions: has_verified_email
True     23
False     1
Name: has_verified_email, dtype: int64


The value counts of the users with the most contributions: is_mod
True     14
False    10
Name: is_mod, dtype: int64


The value counts of the users with the most contributions: is_gold
False    19
True      5
Name: is_gold, dtype: int64


The value counts of the users with the most contributions: is_banned
False    22
True      2
Name: is_banned, dtype: int64


The min of comment_karma -1.0


The max of comment_karma 280938.0


The mean of comment_karma 32688.95


The median of comment_karma 32688.95


The min of link_karma 860.0


The max of link_karma 2838485.0


The mean of link_karma 386394.59


The median of link_karma 386394.59


The value counts of the users with the most contributions: banned_unverified
others        21
banned         2
unverified     1
Name: banned_unverified, dtype: int64


The value counts of the users with the most contributions: creation_year
others    18
2018       4
banned     2
Name: creation_year, dtype: int64


# pd.set_option('display.max_colwidth', None)
suspected_dict = {}


df_fuc = df[df.text.str.lower().str.contains('fuck')]
print(df_fuc.shape)
df_fuc.head()

(225, 17)


# get the authors of these submissions having 'fuck_amber_heard'

mask = (df['submission_text'] == 'fuck_amber_heard') & (df['submission_comment'] == 'submission')
df_sub = df[mask]
print(df_sub.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_sub.head())

(0, 17)


# get the authors of these submissions having the same submission text

mask = (df['submission_text'].str.contains('fuck')) & (df['submission_comment'] == 'submission')
df_sub_fuc = df[mask]
print(df_sub_fuc.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_sub_fuc.head())

(15, 17)


df_sub_fuc_contributions = df_sub_fuc.groupby(df_sub_fuc.created_at.dt.date).size().reset_index(name='n_contributions')

fig = px.bar(df_sub_fuc_contributions,
             x='created_at', 
             y='n_contributions', title='The number of submissions with the word "F*CK" in 2018')
fig.update_traces(marker_color='red', marker_line_width=.5, opacity=1, textposition='auto') 
# , marker_line_color='#5296dd'

fig.show()


df_sub_fuc_contributions.sort_values('n_contributions', ascending=False)


df_fuc.author.value_counts().head(10)

-banned-            65
Count_Fapula1        4
DariJC               3
TEDHARDYLEEN         2
TocTheElder          2
dirtydegrading       2
RedditZacuzzi        2
cornylamygilbert     2
chi_dist90           2
arsenalmat           2
Name: author, dtype: int64


df_fuc.submission_comment.value_counts()

comment       206
submission     19
Name: submission_comment, dtype: int64


df_fuc.subreddit.value_counts().head(10)

r/JerkOffToCelebs      54
r/gentlemanboners      18
r/WatchItForThePlot    17
r/movies               14
r/Celebs               12
r/celebJObuds          11
r/celebnsfw            10
r/MensRights           10
r/DC_Cinematic          9
r/goddesses             8
Name: subreddit, dtype: int64


df_fuc.created_at.dt.date.value_counts().head(10)

2018-12-19    14
2018-12-20    10
2018-05-02     5
2018-06-06     5
2018-08-15     5
2018-12-28     5
2018-08-11     5
2018-07-03     4
2018-11-19     4
2018-08-10     4
Name: created_at, dtype: int64


df_fuc_contributions = df_fuc.groupby(df_fuc.created_at.dt.date).size().reset_index(name='n_contributions')

fig = px.bar(df_fuc_contributions,
             x='created_at', 
             y='n_contributions', title='The number of "F*CK" contributions in 2018')
fig.update_traces(marker_color='red', marker_line_width=1, opacity=1, textposition='auto') 
# , marker_line_color='#5296dd'

fig.show()


df_fuc_contributions.sort_values('n_contributions', ascending=False).head(10)


df_count = df.query(" author == 'Count_Fapula1' ")
print(df_count.shape)
df_count.head()

(16, 17)


df_count.created_at.dt.date.value_counts()

2018-12-05    4
2018-12-20    4
2018-12-26    2
2018-12-18    2
2018-12-23    1
2018-12-14    1
2018-12-29    1
2018-12-24    1
Name: created_at, dtype: int64


df_count.text.value_counts().head(3)

Amber, because I’d rather fuck Scarlett’s sweet pussy.               1
I can’t wait to see it. God, she looks so good with that red hair    1
Still looking for a bud to RP as Amber Heard and help me cum         1
Name: text, dtype: int64


df_count[df_count.author == 'Count_Fapula1'].submission_comment.value_counts()

comment       13
submission     3
Name: submission_comment, dtype: int64


df_count[df_count.author == 'Count_Fapula1'].text.value_counts()

Amber, because I’d rather fuck Scarlett’s sweet pussy.                                                  1
I can’t wait to see it. God, she looks so good with that red hair                                       1
Still looking for a bud to RP as Amber Heard and help me cum                                            1
Fuck, she's gorgeous.                                                                                   1
Oh geez... I need to see this fucking movie.                                                            1
Right? Sometimes I browse r/Amber_Heard and I'm totally smitten. Jesus Christ...                        1
God, she looks so sexy in this suit...                                                                  1
Is she on top of me like she is in the gif? Because if that's the case, we're going full on cowgirl.    1
What I'd give to have Amber Heard worshipping my cock                                                   1
Need a bud to RP as Amber Heard for me                                                                  1
Those lips 🤤                                                                                            1
Me fuckin too, man                                                                                      1
Right? She looks like the type that just *loves* sucking cock                                           1
God, she's so sexy                                                                                      1
Imagine her in this suit with a ton of cum dripping off her chin and down her cleavage...               1
Amber is one of my favorites to cum for, you've been missing out...                                     1
Name: text, dtype: int64


df_dari = df.query(" author == 'DariJC' ")
print(df_dari.shape)
df_dari.head()

(11, 17)


df_dari.created_at.dt.date.value_counts()

2018-11-19    2
2018-09-27    2
2018-11-04    2
2018-08-27    1
2018-11-14    1
2018-12-31    1
2018-08-29    1
2018-01-20    1
Name: created_at, dtype: int64


df_dari.text.value_counts().head(3)

Unzip her and have her bend over slightly, bracing against the window.  Put on a show for everyone going by...                                                                                                                   2
She def looks like she’s into the rough stuff                                                                                                                                                                                    1
Giving her a rough fuck would be incredibly fun.  Doggystyle, smacking her ass making her moan and scream.  Seeing her lay on her stomach, breathing hard recovering afterwards, her blonde hair messily covering her face...    1
Name: text, dtype: int64


df_dari[df_dari.author == 'DariJC'].submission_comment.value_counts()

comment    11
Name: submission_comment, dtype: int64


df_dari[df_dari.author == 'DariJC'].text.value_counts()

Unzip her and have her bend over slightly, bracing against the window.  Put on a show for everyone going by...                                                                                                                   2
She def looks like she’s into the rough stuff                                                                                                                                                                                    1
Giving her a rough fuck would be incredibly fun.  Doggystyle, smacking her ass making her moan and scream.  Seeing her lay on her stomach, breathing hard recovering afterwards, her blonde hair messily covering her face...    1
Needa tear off those panties and take her right there                                                                                                                                                                            1
She’d be an incredible fuck, especially looking like she does in this pic                                                                                                                                                        1
She would be a really fun fuck...                                                                                                                                                                                                1
Lightly push her forward and lift that dress up over her ass...                                                                                                                                                                  1
Very slutty look to her                                                                                                                                                                                                          1
She’d be great lol.  Reminds me of Gwen Diamond in her IR scenes                                                                                                                                                                 1
Love the push up she’s got going on in this movie                                                                                                                                                                                1
Name: text, dtype: int64


df.text.value_counts().head(25)

Amber Heard                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            1045
[deleted]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               171
What?  What did she hear?                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               121
[removed]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                52
[Aquaman] Amber Heard                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    47
Amber Heard - The Informers                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              20
Amber heard                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              14
amber heard                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              13
Your submission has been automatically removed for not including a valid category/subcategory tag. Tags are essential to an optimal browsing experience for our users.\n\nSince your post was removed automatically, you are free to resubmit it with an appropriate tag. You can find the tagging guide [here](/r/DC_Cinematic/wiki/linkflair#wiki_automated_tagging). Add a valid and appropriate tag in your submission title. Choose wisely, as posts with misleading tags are subject to removal.\n\n**Message the moderators if your post was removed despite being tagged with an input from the category list.**\n\n\n*I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/DC_Cinematic) if you have any questions or concerns.*                                                                                                                                                                                                                                                                                                                                                  12
The following links may contain NSFW material. Some links may not work correctly in mobile Reddit apps.\r\n\r\n---\r\n\r\n**Amber Heard**\r\n\r\n[Instagram](https://www.instagram.com/amberheard_)\r\n\r\n[Twitter](https://www.Twitter.com/realamberheard)\r\n\r\n[YouTube](https://www.youtube.com/channel/UCB2ySslhqSI7Vd2nx0UHvSw)\r\n\r\n[Facebook](https://www.Facebook.com/AmberHeardOfficial)\r\n\r\n/r/Amber_Heard\r\n\r\n[IMDB](https://www.IMDB.com/name/nm1720028)\r\n\r\n[Google Image Search](https://www.google.com/search?tbm=isch&q=Amber+Heard)\r\n\r\n[YouTube Search](https://www.youtube.com/results?search_query=Amber+Heard)\r\n\r\n[More photos in this subreddit](https://www.reddit.com/r/BeautifulFemales/search?q=Amber+Heard&restrict_sr=on&sort=new&t=all)\r\n\r\nIf you know of more social media info for this person, reply to this comment with the relevant information and it will be added to the bot's database.\r\n\r\n---\r\nThis was posted by a bot. If you discover a problem with it or have suggestions on how to improve it, please [contact LittleMisfit](https://www.reddit.com/message/compose/?to=littlemisfit).       9
Pinned to Amber Heard on Pinterest                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        8
What did she hear?                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        8
Amber Heard - The Informers (2008)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        7
MFK                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       7
Amber Heard - London Fields                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               6
Wow                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       6
Her name is a complete sentence                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           6
amber heard lifestyle                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     5
Source?                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   5
What did Amber hear?                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      5
LONDON FIELDS Official Trailer (2018) Amber Heard, Johnny Depp Movie HD                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   5
Amber                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     5
MKF                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       5
Kik: MrBate247                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            5
What? What did she hear?                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  4
Name: text, dtype: int64


text = "what did she hear"

df_hear = df[df.text.str.lower().str.contains(text)]

print(df_hear.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_hear.head())

(148, 17)


df_hear.subreddit.value_counts()

r/Celebs              131
r/goddesses             6
r/celebnsfw             2
r/sketches              1
r/CelebrityArmpits      1
r/WtSSTaDaMiT           1
r/CelebrityButts        1
r/Celebhub              1
r/BeautifulFemales      1
r/DCEUboners            1
r/PrettyGirls           1
r/CelebrityFeet         1
Name: subreddit, dtype: int64


df_hear.author.value_counts()

Night_Chicken       128
MyKey18               1
murph420              1
VVombatCombat         1
brownsatin            1
christpie             1
Matty_tt              1
PigeonMan45           1
omre16                1
iplayinv3rtd          1
HollowedHunter        1
no_di                 1
Augustus420           1
cynicaldotes          1
moemoolah37           1
Sonmeisterbank        1
HEYL1STEN             1
ArtemisSkrivey        1
ImJumentous           1
predictablePosts      1
empifer               1
Name: author, dtype: int64


df_hear[df_hear.author == 'Night_Chicken'].submission_comment.value_counts()

comment    128
Name: submission_comment, dtype: int64


df_hear[df_hear.author == 'Night_Chicken'].subreddit.value_counts()

r/Celebs    128
Name: subreddit, dtype: int64


df_hear_contributions = df_hear.groupby(df_hear.created_at.dt.date).size().reset_index(name='n_contributions')


fig = px.bar(df_hear_contributions,
             x='created_at', 
             y='n_contributions', title='The number of "What? What did she hear?" contributions in 2018')
fig.update_traces(marker_color='red', marker_line_width=1, opacity=1, textposition='auto') 
# , marker_line_color='#5296dd'

fig.show()


df_hear_contributions.sort_values('n_contributions', ascending=False).head(5)


df_hear[df_hear.created_at.dt.date.astype(str) == '2018-08-29'].author.value_counts()

Night_Chicken    103
Name: author, dtype: int64


df_hear[df_hear.created_at.dt.date.astype(str) == '2018-08-29'].submission_comment.value_counts()

comment    103
Name: submission_comment, dtype: int64


df_hear[df_hear.created_at.dt.date.astype(str) == '2018-08-29'].subreddit.value_counts()

r/Celebs    103
Name: subreddit, dtype: int64


max_t = df_hear[df_hear.created_at.dt.date.astype(str) == '2018-08-29'].created_at.dt.time.max() 
max_t

datetime.time(4, 8, 48)


min_t = df_hear[df_hear.created_at.dt.date.astype(str) == '2018-08-29'].created_at.dt.time.min()
min_t

datetime.time(3, 41, 58)


from datetime import datetime, date

datetime.combine(date.today(), max_t) - datetime.combine(date.today(), min_t)

datetime.timedelta(seconds=1610)


1610 / 60

26.833333333333332


text = "[aquaman] amber heard"

df_aqua = df[df.text.str.lower().str.contains(text)]

print(df_aqua.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_aqua.head())

(30, 17)


df_aqua.subreddit.value_counts()

r/DC_Cinematic        5
r/news                3
r/newstweetfeed       2
r/worldnews           2
r/Elon_musketeers     2
r/movies              2
r/AutoNewspaper       1
r/Amber_Heard         1
r/dirtypenpals        1
r/EnoughMuskSpam      1
r/morganwade          1
r/pickoneceleb        1
r/goddesses           1
r/FOXauto             1
r/removalbot          1
r/Spillthetea         1
r/entertainment       1
r/TwoXChromosomes     1
r/REPORT_ITALIANO     1
r/moviescirclejerk    1
Name: subreddit, dtype: int64


df_aqua.author.value_counts()

-banned-               5
BSRussell              2
ZorakLocust            2
jeff98379              2
iDevice_Help           2
trendynewsupdate       1
PHANTOMCREEPER         1
report_italiano        1
OwlWayneOwlwards       1
illegitimatemexican    1
Chronos2016            1
KrishAndChips          1
Nuggetry               1
Baramos_               1
BrkntKlc               1
morganwade             1
removalbot             1
viralreportnow         1
AutoNewspaperAdmin     1
AutoNewsAdmin          1
nomnomnomhangry        1
worldwide__master      1
Name: author, dtype: int64


text = "Amber Heard - The Informers".lower()

df_inf = df[df.text.str.lower().str.contains(text)]

print(df_inf.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_inf.head())

(27, 17)


df_inf.subreddit.value_counts()

r/WatchItForThePlot    8
r/celebnsfw            3
r/celebsnaked          3
r/CelebSexScenes       2
r/Celebsnudess         2
r/Amber_Heard          2
r/PopCultureGifs       2
r/CelebsGW             1
r/nsfwcelebgifs        1
r/adultgifs            1
u/pccpux               1
r/Celebhub             1
Name: subreddit, dtype: int64


df_inf.author.value_counts()

-banned-      9
Ezio9619      8
vonmark955    4
vonjobi951    2
GRJR721       2
CelebsGW      1
vonjobi956    1
Name: author, dtype: int64


df_inf[df_inf.author == 'Ezio9619'].subreddit.value_counts()

r/celebsnaked          3
r/WatchItForThePlot    3
r/celebnsfw            1
r/Celebhub             1
Name: subreddit, dtype: int64


df_inf[df_inf.author == 'Ezio9619'].created_at.dt.date.value_counts()

2018-07-24    4
2018-08-14    4
Name: created_at, dtype: int64


text = "Amber Heard".lower()

df_ah = df[df.text.str.lower().str.contains(text)]

print(df_ah.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_ah.head())

(2213, 17)


df_ah.subreddit.value_counts()

r/Celebs                 350
r/gentlemanboners        296
r/DCEUboners              87
r/DC_Cinematic            85
r/goddesses               79
                        ... 
r/jessicaalba              1
r/OnOff                    1
r/PickHerOutfit            1
r/JerkOffToDesiCelebs      1
r/celebdominatrixs         1
Name: subreddit, Length: 301, dtype: int64


df_ah.author.value_counts()

-banned-              829
AutoNewsAdmin          35
emilyguy               34
AutoNewspaperAdmin     33
ccrraapp               27
                     ... 
sonofcross              1
filmitalkies            1
JoLuGaLo                1
s0mnambulance           1
Clip_Dirtblade          1
Name: author, Length: 605, dtype: int64


df_ah[df_ah.author == 'AutoNewsAdmin'].subreddit.value_counts()

r/USATODAYauto        9
r/REUTERSauto         4
r/ABCauto             4
r/FOXauto             3
r/LATIMESauto         2
r/MIAMIHERALDauto     2
r/RTauto              1
r/SCMPauto            1
r/WAPOauto            1
r/NYTauto             1
r/INDEPENDENTauto     1
r/NEWSDAYauto         1
r/TWTauto             1
r/CBSauto             1
r/HOUSTONCHRONauto    1
r/BBCauto             1
r/NZHauto             1
Name: subreddit, dtype: int64


df_ah[df_ah.author == 'AutoNewsAdmin'].created_at.dt.date.value_counts()

2018-10-25    5
2018-10-22    4
2018-07-03    3
2018-12-06    2
2018-10-03    2
2018-04-05    2
2018-12-05    2
2018-12-19    2
2018-11-26    2
2018-06-25    1
2018-10-29    1
2018-09-14    1
2018-10-10    1
2018-12-03    1
2018-10-04    1
2018-11-27    1
2018-05-15    1
2018-12-20    1
2018-11-13    1
2018-12-12    1
Name: created_at, dtype: int64


df_ah[df_ah.author == 'emilyguy'].subreddit.value_counts()

r/gentlemanboners      19
r/Celebs                7
r/celebsnaked           4
r/celebnsfw             2
r/WatchItForThePlot     1
r/CaraDelevingne        1
Name: subreddit, dtype: int64


df_ah[df_ah.author == 'emilyguy'].created_at.dt.date.value_counts()

2018-03-22    3
2018-05-05    3
2018-10-05    2
2018-11-15    1
2018-11-01    1
2018-03-08    1
2018-06-11    1
2018-09-25    1
2018-12-22    1
2018-03-13    1
2018-05-16    1
2018-12-16    1
2018-05-01    1
2018-09-17    1
2018-05-25    1
2018-12-29    1
2018-08-26    1
2018-03-19    1
2018-07-04    1
2018-07-05    1
2018-03-24    1
2018-08-18    1
2018-07-23    1
2018-07-03    1
2018-06-28    1
2018-07-12    1
2018-04-13    1
2018-06-19    1
2018-10-17    1
Name: created_at, dtype: int64


text = "fuck amber heard"

df_fuc2 = df[df.text.str.lower().str.contains(text)]

print(df_fuc2.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_fuc2.head())

(4, 17)


df_fuc2.author.value_counts()

-banned-    4
Name: author, dtype: int64


df_fuc2.subreddit.value_counts()

r/goddesses    2
r/movies       2
Name: subreddit, dtype: int64


df_fuc2.submission_comment.value_counts()

comment    4
Name: submission_comment, dtype: int64


df_nc = df.query(" author == 'Night_Chicken'")
df_nc = df_nc.sort_values('created_at')

print(df_nc.shape)
df_nc.head(2)

(135, 17)


df_nc.text.value_counts()

What?  What did she hear?                                                                                           121
What? What did she hear?                                                                                              4
I want to know as well.                                                                                               1
What?  Thank you!                                                                                                     1
Excellent!  Not as exciting as the crunching and jostling sounds in Elon's full pockets which escaped her grasp.      1
Exactly.\n\n&#x200B;                                                                                                  1
What?   What did she hear?                                                                                            1
What?  What did she hear?                                                                                             1
Good question.\n\n&#x200B;                                                                                            1
What?  What did she hear? Oh.  The Birthday song.                                                                     1
Yes.  I want to know.                                                                                                 1
Yes!  What?                                                                                                           1
Name: text, dtype: int64


df['text_words'].value_counts().head(10);


px.histogram(df['text_words'].to_frame(), x="text_words",title='number of words in each contribution',
            nbins=200).update_traces(marker_color='#5296dd')


px.bar(data_frame=df['top_level'].value_counts().to_frame().reset_index(),
       x="index", y="top_level").update_layout(title='Comment or Submission (Top Level of Contrbution "Parent")',
                   xaxis_title='contribution top level (parent) category',
                   yaxis_title='number of contributions').update_traces(marker_color='#5296dd')


df['submission_text'].value_counts().head(20)

amber_heard                                          2490
DC_Cinematic                                          760
amber_heard_received_death_threats_and_was            254
amber_heard_the_informers                             167
just_a_friendly_reminder_that_domestic_abuse_is       102
amber_heard_fans_upset_after_she_posted_a_racist       96
johnny_depp_claims_ex_amber_heard_punched_him          95
amber_heard_mirin_jason_momoa                          77
johnny_depp_accuses_amber_heard_of_shitting_in         76
amber_heard_london_fields_2018                         71
first_poster_london_fields_amber_heard_billy_bob       69
aquaman_actress_amber_heard_gets_called_racist         65
aquaman_amber_heard                                    64
new_aquaman_images_shows_jason_momoa_and_amber         63
amber_heard_film_london_fields_suffers_one_of_the      61
amber_heard_has_an_amazing_body                        60
2018_comiccon_red_carpet_gal_gadot_melissa             51
amber_heard_stunning_in_red                            47
personally_i_think_it_takes_a_lot_of_skill_to          44
amber_heard_in_the_informers                           43
Name: submission_text, dtype: int64


df_amber = df.query(" submission_text == 'amber_heard' & \
                           submission_comment == 'submission' ")
print(df_amber.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_amber.head())

(1044, 17)


li = list(df_amber.author.unique())
li.remove('-banned-')
df_users[df_users.user_name.isin(li)]


df_mera_comments = df.query(" submission_text == 'amber_heard' ")
print(df_mera_comments.shape)
df_mera_comments.head(1)

(2490, 17)


df_mera_contributions = df_mera_comments.groupby(df_mera_comments.created_at.dt.date).size().reset_index(name='n_contributions')


fig = px.bar(df_mera_contributions.head(7),
             x='created_at', 
             y='n_contributions', title='The number of contributions/date on these submissions')

fig.update_layout(
    xaxis = dict(
        title='Contribution Date',
        tickmode = 'array',
        tickvals = df_mera_contributions.head(7).created_at,
    )
)

clrs = ['red' if (y > 200) else '#5296dd' for y in df_mera_contributions.n_contributions] 

fig.update_traces(marker_color=clrs, marker_line_width=1.5, opacity=1, textposition='auto')

fig.show()


df_mera_authors = df_mera_comments.groupby(df_mera_comments.author).size().reset_index(name='n_contributions')


fig = px.bar(df_mera_authors,
             x='author', 
             y='n_contributions', title='The number of contributions per author on these submissions')
fig.update_traces(marker_color='#5296dd', opacity=1, textposition='auto') 
# , marker_line_color='#5296dd', marker_line_width=2

fig.update_yaxes(range = [0,25])

fig.show()


df_mera_comments[df_mera_comments.author == 'Night_Chicken']


df_mera_comments[df_mera_comments.author == 'emilyguy'].shape

(34, 17)


df_dc = df.query(" submission_text == 'DC_Cinematic' & \
                           submission_comment == 'submission' ")
print(df_dc.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_dc.head())

(47, 17)


li = list(df_dc.author.unique())
df_users[df_users.user_name.isin(li)]


df_dc_comments = df.query(" submission_text == 'DC_Cinematic' & \
                           submission_comment == 'comment' ")
print(df_dc_comments.shape)
df_dc_comments.head(1)

(713, 17)


df_dc_comments = df_dc_comments.groupby(df_dc_comments.created_at.dt.date).size().reset_index(name='n_contributions')


fig = px.bar(df_dc_comments.head(4),
             x='created_at', 
             y='n_contributions', title='The number of contributions/date on these submissions')

fig.update_layout(
    xaxis = dict(
        title='Contribution Date',
        tickmode = 'array',
        tickvals = df_dc_comments.head(4).created_at,
    )
)

clrs = ['red' if (y > 200) else '#5296dd' for y in df_dc_comments.n_contributions] 

fig.update_traces(marker_color=clrs, marker_line_width=1.5, opacity=1, textposition='auto')

fig.show()


df_dc_comments = df.query(" submission_text == 'DC_Cinematic' & \
                           submission_comment == 'comment' ")


df_dc_authors = df_dc_comments.groupby(df_dc_comments.author).size().reset_index(name='n_contributions')
df_dc_authors.sort_values('n_contributions', ascending=False).head(10)


fig = px.bar(df_dc_authors,
             x='author', 
             y='n_contributions', title='The number of contributions per author on these submissions')
fig.update_traces(marker_color='#5296dd', opacity=1, textposition='auto') 
# , marker_line_color='#5296dd', marker_line_width=2

fig.update_yaxes(range = [0,25])

fig.show()


df_dc_comments[df_dc_comments.author == 'ZorakLocust'].text.head(5).values

array(['Could you perhaps elaborate on what’s so dumb about it? ',
       'That’s not what her Wikipedia page says. ',
       'Notice that I put “abused” in quotations. It’s because I don’t believe she’s an actual abuse victim. I think she’s a manipulative gold digger who wanted some easy publicity. This is in no way meant to discredit abuse victims but anyone who blindly believes that Heard is one of them is not doing anyone any favors. ',
       'I would only be victim blaming if Amber Heard actually was a victim. ',
       'I addressed your point about how no one would want to be an abuse victim. My response was that while that’s true, there is evidence to suggest that Amber Heard is not a victim of anything. \n\nAlso, if you want an answer as to why she would wait a year (because that’s how long they were married) before accusing Depp of domestic abuse, I don’t know the answer to that. Still, it seems suspicious as hell that she was apparently demanding money out of him. \n\nAnyway, would you perhaps care to address what I mentioned about the video she posted to “prove” she was abused? Specifically how it doesn’t actually show Johnny Depp abusing her? '],
      dtype=object)


df_dc_comments[df_dc_comments.author == 'Chronos2016'].text.head(5).values

array(['Not a big change acting wise but she would look cool as her too. ',
       "no it's a wig.",
       "I remember being in like 9th grade when Amber Heard came out as bisexual. It was 2008 and it was a pretty major thing for a celebrity to say. That was my first introduction to her.\n\nI really liked her makeup looks and her fashion back then and she's probably one of the celebs who got me into makeup. Her ad campaign for Guess was also iconic and the campaign video is often used in fan videos for leaked Lana Del Rey songs. \n\nShe's one of the more glamorous celebs out there and she super well loved amongst the sad indie girl circles online.",
       "I think she played Seth Rogen's gf in Pineapple Express. It was a pretty small and thankless role. ",
       "Don't do that. They both put out a joint statement saying that both sides told the truth. Johnny is being petty and going back on that statement. \n\nWe may never know what happened between the two of them, clearly it was a toxic relationship. Amber is trying her best to move on but Johnny still wants to drag this out. He should take a page from Amber and move on himself."],
      dtype=object)


df_death = df.query(" submission_text == 'amber_heard_received_death_threats_and_was' & \
                           submission_comment == 'submission' ")
print(df_death.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_death.head())

(1, 17)


df_death.permalink.values[0]

'/r/MensRights/comments/a7lypo/amber_heard_received_death_threats_and_was/'


li = list(df_death.author.unique())
df_users[df_users.user_name.isin(li)]


df_death_comments = df.query(" submission_text == 'amber_heard_received_death_threats_and_was' & \
                           submission_comment == 'comment' ")
print(df_death_comments.shape)
df_death_comments.head(1)

(253, 17)


df_death_comments = df_death_comments.groupby(df_death_comments.created_at.dt.date).size().reset_index(name='n_contributions')


fig = px.bar(df_death_comments.head(4),
             x='created_at', 
             y='n_contributions', title='The number of contributions/date on these submissions')

fig.update_layout(
    xaxis = dict(
        title='Contribution Date',
        tickmode = 'array',
        tickvals = df_death_comments.head(4).created_at,
    )
)

clrs = ['red' if (y > 200) else '#5296dd' for y in df_death_comments.n_contributions] 

fig.update_traces(marker_color=clrs, marker_line_width=1.5, opacity=1, textposition='auto')

fig.show()


df_death_comments = df.query(" submission_text == 'amber_heard_received_death_threats_and_was' & \
                           submission_comment == 'comment' ")


df_death_authors = df_death_comments.groupby(df_death_comments.author).size().reset_index(name='n_contributions')
df_death_authors.sort_values('n_contributions', ascending=False).head(10)


fig = px.bar(df_death_authors,
             x='author', 
             y='n_contributions', title='The number of contributions per author on these submissions')
fig.update_traces(marker_color='#5296dd', opacity=1, textposition='auto') 
# , marker_line_color='#5296dd', marker_line_width=2

fig.update_yaxes(range = [0,25])

fig.show()


df_death_comments[df_death_comments.author == 'tenchineuro'].text.head(5).values

array(["> Explaining that she rarely left her apartment for fear of being hounded, she added: **'I felt as though I was on trial in the court of public opinion** - and my life and livelihood depended on myriad judgments far beyond my control.'\n\nWelcome to the world #metoo created.\n\nI suspect that she's exaggerating the career effects though, apparently it's OK for female actresses to sexually assault or assault their husbands/BFs.",
       "> Yes, the burden of proof is on the accuser, but not having proof does not mean her allegations are false.\n\nIs that your default assumption? For how long have you been a feminist?\n\n> However, when someone qualifies those allegations as false, they are now making a new allegation about the initial allegations. The burden of proof - of proving the allegations are actually false, is now on them.\n\nSo if you deny the allegations then the burden is now on you to prove your innocence? You'd make a fine feminist lawyer, or maybe you are already?",
       "> The allegations being unsubstantiated and the allegations being provably false are extremely different.\n\nThey don't have to be provably false, it's impossible to prove that something did not happen, they need to be proven true beyond the benefit of a doubt.",
       '>> However, when someone qualifies those allegations as false, they are now making a new allegation about the initial allegations. The burden of proof - of proving the allegations are actually false, is now on them.\n\n> I\'m discussing how our criminal justice system is "supposed" to work and the philosophy behind it.\n\nNo, you are not, it is never the legal responsibility of the accused to prove their innocence. \n\nAnd claiming one\'s innocence does not create a burden of proving it.\n\nWhat you are saying is basically feminist jurisprudence.',
       '>>  They don\'t have to be provably false\n>\n> In order to be called "false allegations" they do.\n\nSo you believe that innocence needs to be proven, or what, you assume guilt? You are a fine advocate for false accusers BTW, you have lots of company on the feminist side of the fence.'],
      dtype=object)


df_death_comments[df_death_comments.author == 'tenchineuro'].created_at.dt.date.value_counts()

2018-12-19    22
Name: created_at, dtype: int64


df_death_comments[df_death_comments.author == 'tenchineuro'].subreddit.value_counts()

r/MensRights    22
Name: subreddit, dtype: int64


df.parent_id.value_counts().head()

t3_91hqrc    49
t3_a6i1j4    40
t3_a7lypo    36
t3_9uzs60    34
t3_8hx1bz    30
Name: parent_id, dtype: int64


fig = px.bar(df.parent_id.value_counts().to_frame().head(25).reset_index(), x="parent_id", y="index",
             height=500,
             title='sumbissions with most comments (Top Level Comments)').update_layout(
                   xaxis_title='Number of comments',
                   yaxis_title='subbredit').update_traces(marker_color='#5296dd')

fig.update_yaxes(autorange="reversed")


df_top5 = df_merged[df_merged.child_id.isin(df_merged.parent_id.value_counts().head().index)]

with pd.option_context('display.max_colwidth', None):
  display(df_top5)


df_merged.submission_text = df_merged.submission_text.str.replace('_', ' ')


# get a list with the top 5 submission text
top5_text = list(df_top5.text)


def compare(str):
    for text in top5_text:
        if str in text:
            return True
        else: return False


mask = df_merged.submission_text.apply(compare)

df_top5_contributions1 = df_merged[mask]

df_top5_authors = df_top5_contributions1.groupby(df_top5_contributions1.user_name).size().reset_index(name='n_contributions')

fig = px.bar(df_top5_authors,
             x='user_name', 
             y='n_contributions', title='The number comments per author on these submissions')

fig.update_traces(marker_color='#5296dd', opacity=1, textposition='auto') 

fig.show()


df_top5_contributions1.shape

(33, 24)


df_top5_contributions2 = df_merged[df_merged.parent_id.isin(df_merged.parent_id.value_counts().head().index)]

df_top5_authors = df_top5_contributions2.groupby(df_top5_contributions2.user_name).size().reset_index(name='n_contributions')


fig = px.bar(df_top5_authors,
             x='user_name', 
             y='n_contributions', title='The number of parent comments per author on these submissions')
fig.update_traces(marker_color='#5296dd', opacity=1, textposition='auto') 
# , marker_line_color='#5296dd', marker_line_width=2

fig.update_yaxes(range = [0,5])

fig.show()


df_top5_contributions2.shape

(189, 24)


df_submissions = df[df.submission_comment == 'submission']
print(df_submissions.shape)
df_submissions.head(2)

(2000, 17)


df_submissions.author.value_counts().nlargest(n=10)

-banned-              794
AutoNewsAdmin          35
emilyguy               34
AutoNewspaperAdmin     33
ccrraapp               27
Rednaxela117           25
jeff98379              21
vonmark955             20
InfiniTitans           18
ZadocPaet              17
Name: author, dtype: int64


df_submissions.author.value_counts().to_frame().head(10)


fig = px.bar(df_submissions.author.value_counts().to_frame().head(10).reset_index(), x="author", y="index",
             height=500,
             title='Authors with most Submissions').update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='Number of Negative Submissions',
                   yaxis_title='Author_Name').update_traces(marker_color='#5296dd')

fig.update_yaxes(autorange="reversed")


df_submissions.author.value_counts().head()

-banned-              794
AutoNewsAdmin          35
emilyguy               34
AutoNewspaperAdmin     33
ccrraapp               27
Name: author, dtype: int64


check_list = df_submissions.author.value_counts().nlargest(n=25).index.tolist()[1:]
check_list

['AutoNewsAdmin',
 'emilyguy',
 'AutoNewspaperAdmin',
 'ccrraapp',
 'Rednaxela117',
 'jeff98379',
 'vonmark955',
 'InfiniTitans',
 'ZadocPaet',
 'MightUlt-7',
 'FlexOutlaw',
 'Queen1110',
 'AngelaStettner69',
 'Pm-me-your-ass-photo',
 'GRJR721',
 'naughtytwd',
 'vonjobi951',
 'Ezio9619',
 'horny_fuckers',
 'Luke5to1',
 'iDevice_Help',
 'pitsnbush',
 'windowmedia',
 'sagar7854']


# get a data frame with the most negative-comments users
df_check = df_users[df_users['user_name'].isin(check_list)]
print(df_check.shape)
df_check.head(2)

(24, 10)


df_check['user_name'].nunique()

24


get_stats(df_check)

The value counts of the users with the most contributions: has_verified_email
True     22
False     2
Name: has_verified_email, dtype: int64


The value counts of the users with the most contributions: is_mod
True     18
False     6
Name: is_mod, dtype: int64


The value counts of the users with the most contributions: is_gold
False    15
True      9
Name: is_gold, dtype: int64


The value counts of the users with the most contributions: is_banned
False    17
True      7
Name: is_banned, dtype: int64


The min of comment_karma -1.0


The max of comment_karma 280938.0


The mean of comment_karma 23877.53


The median of comment_karma 23877.53


The min of link_karma 242.0


The max of link_karma 2838485.0


The mean of link_karma 529420.82


The median of link_karma 529420.82


The value counts of the users with the most contributions: banned_unverified
others        15
banned         7
unverified     2
Name: banned_unverified, dtype: int64


The value counts of the users with the most contributions: creation_year
others    13
banned     7
2018       4
Name: creation_year, dtype: int64


df['urls'].nunique()

113


df[df.astype(str)['urls'] != '[]'].head(2)


df['urls'].astype('str').value_counts().head()

[]                             6761
['https://t.co']                 22
['https://www.reddit.com']       21
['https://youtu.be']             10
['https://www.youtube.com']       9
Name: urls, dtype: int64


# the value counts of the # of urls
df['urls_count'].value_counts();


fig = px.histogram(df['urls_count'].to_frame(), x="urls_count",title='Count of the number of URLS in each Contribution',
            nbins=130).update_traces(marker_color='#5296dd')

fig.update_layout(
    xaxis = dict(
        tickmode = 'array',
        tickvals = df['urls_count'],
    )
)

fig.show()


px.histogram(df[~(df['urls_count'].isin([0,60,20,16]))], x="urls_count",title='Count of the number of URLS in each Contribution',
            nbins=20).update_traces(marker_color='#5296dd')


fig = px.histogram(df['submission_words'].to_frame(), x="submission_words",
                   title='number of words in submission text',
                   nbins=50).update_traces(marker_color='#5296dd')

fig.update_layout(
    xaxis = dict(
        title='Number of submission words',
        tickmode = 'linear',
    )
)


df['subreddit'].nunique()

302


df['subreddit'] = df['subreddit'].str[:]


df.subreddit.value_counts().to_frame().head(20).reset_index()


fig = px.bar(df.subreddit.value_counts().to_frame().head(20).reset_index(), x="subreddit", y="index",
             height=500,
             title='Most used subbredits').update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='Number of comments',
                   yaxis_title='subbredit').update_traces(marker_color='#5296dd')

fig.update_yaxes(autorange="reversed")


# note that value_counts() neglect Zeros
df_merged["days_after_creation"].value_counts()

1582.0    104
2599.0     37
2430.0     36
2449.0     35
487.0      31
         ... 
3422.0      1
1740.0      1
919.0       1
923.0       1
1174.0      1
Name: days_after_creation, Length: 2115, dtype: int64


px.histogram(df_merged, x="days_after_creation",title='days_after_creation',
            nbins=250).update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='number of days',)


print('The number of accounts posted the same day they was created!')
df_merged[df_merged['days_after_creation'] == 0].shape[0]

The number of accounts posted the same day they was created!

24


print('The number of accounts posted the same week they was created!')
df_merged[df_merged['days_after_creation'] <= 7].shape[0]

The number of accounts posted the same week they was created!

62


print('The number of accounts posted the same month they was created!')
df_merged[df_merged['days_after_creation'] <= 30].shape[0]

The number of accounts posted the same month they was created!

171


df_merged[df_merged['days_after_creation'] <= 30]['user_created_at'].dt.year.value_counts()

2018    171
Name: user_created_at, dtype: int64


mask = (df_merged['days_after_creation'] <= 30) & (df_merged['user_created_at'].dt.year == 2018)
df_merged[mask]['user_created_at'].dt.strftime('%b').value_counts()

Jun    28
Nov    26
Dec    20
Jul    19
Oct    17
Feb    13
Mar    10
Apr    10
Aug     9
May     7
Sep     7
Jan     5
Name: user_created_at, dtype: int64


months = df_merged[df_merged['days_after_creation'] <= 30]['user_created_at'].dt.strftime('%b')
months_sorted = months.value_counts()[['Jan', 'Feb', 'Mar', 'Apr', 'May']]
months_sorted

Jan     5
Feb    13
Mar    10
Apr    10
May     7
Name: user_created_at, dtype: int64


fig = px.bar(months_sorted,
             x=months_sorted.index, y=months_sorted.values, text=months_sorted.values)

fig.update_layout(
            title={
        'text': "contributions of the accounts posted/commented <br> the same month they were created",
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
        })

# fig.update_layout(
#     xaxis = dict(
#         title='Month(2021)',
#         tickmode = 'array',
#         tickvals = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
#         ticktext = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
#         )
# )

clrs = ['red' if (y > 100) else '#5296dd' for y in months_sorted.values] 

fig.update_traces(marker_color=clrs,
                  marker_line_width=1.5, opacity=1, textposition='auto')
fig.show()


# THE SAME MONTH:
# check for the date these accounts posted/commented 
reddit_30 = df_merged[df_merged['days_after_creation'] <= 30]

dates_count = df_merged.groupby(reddit_30['created_at'].dt.date).size().reset_index(name='contributions')
dates_count.sort_values('contributions', ascending=False);


fig = px.bar(dates_count,
             x='created_at', 
             y='contributions', title = 'contributions of the accounts posted/commented the same month they were created')
fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1, opacity=1, textposition='auto').update_layout()
fig.show()


# THE SAME WEEK
# check for the date these accounts posted/commented
reddit_7 = df_merged[df_merged['days_after_creation'] <= 7]

dates_count_7 = df_merged.groupby(reddit_7['created_at'].dt.date).size().reset_index(name='contributions')
dates_count_7.sort_values('contributions', ascending=False);


fig = px.bar(dates_count_7,
             x='created_at', 
             y='contributions', title = 'contributions of the accounts posted/commented the same week they were created')
fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1, opacity=1, textposition='auto').update_layout()
fig.show()


# THE SAME DAY
# check for the date these accounts posted/commented
reddit_1 = df_merged[df_merged['days_after_creation'] <= 0]

dates_count_1 = df_merged.groupby(reddit_1['created_at'].dt.date).size().reset_index(name='contributions')
dates_count_1.sort_values('contributions', ascending=False);


fig = px.bar(dates_count_1,
             x='created_at', 
             y='contributions', title = 'contributions of the accounts posted/commented the same day they were created')
fig.update_traces(marker_color='#5296dd',
                  marker_line_width=.5, opacity=1, textposition='auto').update_layout()
fig.show()


# get the author names that commented in a negative way the same month the account was created
# to add to the suspected list
df_merged_30 = df_merged.query("days_after_creation <= 30 & sentiment_blob == sentiment_nltk == 'Negative' ")
df_merged_30.head()


# group by creation year and count 
df_contributions = df_merged.groupby(df_merged['user_created_at'].dt.year).size().reset_index(name='n_accounts')

fig = px.bar(df_contributions,
             x='user_created_at', y='n_accounts', text='n_accounts', title='Number of User Accounts Created in each year / having contributions in 2018')
fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1.5, opacity=1, textposition='auto')
fig.show()


fig = px.bar(df.groupby(df['created_at'].dt.month).size().reset_index(name='contribution_count'),
             x='created_at', y='contribution_count', text='contribution_count')

fig.update_layout(
            title={
        'text': "Estimation of the number contributions created in each month of 2018",
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
        })

fig.update_layout(
    xaxis = dict(
        title='Month(2018)',
        tickmode = 'array',
        tickvals = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
        ticktext = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
        )
)

clrs = ['red' if (y > 4000) else '#5296dd' for y in df.groupby(df['created_at'].dt.month).size()] 

fig.update_traces(marker_color=clrs,
                  marker_line_width=1.5, opacity=1, textposition='auto')
fig.show()


fig = px.bar(df.groupby(df['created_at'].dt.day).size().reset_index(name='contribution_count'),
             x='created_at', y='contribution_count', text='contribution_count')

fig.update_layout(
            title={
        'text': "Estimation of the number contributions created in each DayOfMonth in 2018",
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
        })

fig.update_layout(
    xaxis = dict(
        title='Month Days(2018)',
        tickmode = 'linear',
    )
)



clrs = ['red' if (y > 1500) else '#5296dd' for y in df.groupby(df['created_at'].dt.day).size()] 

fig.update_traces(marker_color=clrs,
                  marker_line_width=1.5, opacity=1, textposition='auto')
fig.show()


week_day = df['created_at'].dt.strftime('%a')
# one can sort by any order by providing a custom index explicitely :
# https://stackoverflow.com/questions/43855474/changing-sort-in-value-counts/43855492
week_sorted = week_day.value_counts()[['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']]
week_sorted

Mon     811
Tue    1111
Wed    1406
Thu    1185
Fri     865
Sat     741
Sun     874
Name: created_at, dtype: int64


fig = px.bar(df.groupby(df['created_at'].dt.dayofweek).size().reset_index(name='contribution_count'),
             x='created_at', y='contribution_count', text='contribution_count')

fig.update_layout(
            title={
        'text': "Estimation of the number contributions created in each DayOfWeek (2018)",
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
        })

fig.update_layout(
    xaxis = dict(
        title='DayOfWeek(2018)',
        tickmode = 'array',
        tickvals = [0, 1, 2, 3, 4, 5, 6],
        ticktext = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
        )
)

clrs = ['red' if (y > 4000) else '#5296dd' for y in df.groupby(df['created_at'].dt.dayofweek).size()] 

fig.update_traces(marker_color=clrs,
                  marker_line_width=1.5, opacity=1, textposition='auto')
fig.show()


# check for the hour the contributions were made
df_hours = df.groupby(df['created_at'].dt.hour).size().reset_index(name='contribution_count')
# df_hours.sort_values('contribution_count', ascending=False);   


fig = px.bar(df_hours,
             x='created_at', y='contribution_count', 
             title='Number of contrbutions Comment/Submission in day hours (2018)')

fig.update_layout(
    xaxis = dict(
        title='Hours of Day',
        tickmode = 'linear',
        dtick = 1
    )
)

fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1.5, opacity=1, textposition='auto').update_layout()
fig.show()


df.created_at.dt.date.value_counts().head()

2018-12-19    244
2018-07-03    190
2018-12-20    150
2018-07-22    137
2018-07-24    128
Name: created_at, dtype: int64


trendy_dates = df.groupby(df['created_at'].dt.date).size().reset_index(name='contribution_count')

fig = px.bar(trendy_dates,
             x='created_at', y='contribution_count')

fig.update_layout(
            title={
        'text': "The number of contributions created in each date",
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
        })

fig.update_traces(marker_color='#5296dd',
                  marker_line_width=0.5, opacity=1, textposition='auto').update_layout()
fig.show()


trendy_dates.sort_values('contribution_count', ascending=False)


# get the top 5 trendy dates first, then sort them by date
top_trendy_dates = trendy_dates.sort_values('contribution_count', ascending=False).head(5)
top_trendy_dates.sort_values('created_at', inplace=True)


top_trendy_dates


top_trendy_dates.reset_index(inplace=True)


fig = px.bar(top_trendy_dates,
             x='created_at', y='contribution_count', title='Number of contrbutions Comment/Submission in trendy dates')

fig.update_layout(
    xaxis = dict(
        title='Contribution Date',
        tickmode = 'array',
        tickvals = top_trendy_dates.created_at,
    )
)

clrs = ['red' if (y > 400) else '#5296dd' for y in top_trendy_dates.contribution_count] 

fig.update_traces(marker_color=clrs,
                  opacity=1, textposition='auto').update_layout()

# marker_line_width=1.5,

fig.show()


def peack_days(date):
    print(f'How many users contributed on the peak day ({date})')
    print(df_merged[df_merged.created_at.dt.strftime('%Y-%m-%d') == date].user_name.nunique())
    
    print('Years')
    df_merged_peak1 = df_merged[df_merged.created_at.dt.strftime('%Y-%m-%d') == date]


    # check for the year the accounts were created
    df_user_year1 = df_merged_peak1.groupby(df_merged_peak1['user_created_at'].dt.year).size().reset_index(name='contribution_count')

    fig = px.bar(df_user_year1,
                 x='user_created_at', y='contribution_count', 
                 title=f'The creation year of the accounts contributed on the peak day ({date})')

    fig.update_layout(
        xaxis = dict(
            title='Accout Creation Year',
            tickmode = 'linear',
            dtick = 1
        )
    )

    clrs = ['red' if (y > 250) else '#5296dd' for y in df_user_year1.contribution_count] 

    fig.update_traces(marker_color=clrs,
                      marker_line_width=1.5, opacity=1, textposition='auto').update_layout()
    fig.show()
    
    print('hours')
    
    df_peak_1 = df[df.created_at.dt.strftime('%Y-%m-%d') == f'{date}']

    # check for the hour the contributions were made
    df_hours = df_peak_1.groupby(df['created_at'].dt.hour).size().reset_index(name='contribution_count')
    # df_hours.sort_values('contribution_count', ascending=False);   


    fig2 = px.bar(df_hours,
                 x='created_at', y='contribution_count', 
                 title='Number of contrbutions Comment/Submission in day hours')

    fig2.update_layout(
        xaxis = dict(
            title='Hours of Day',
            tickmode = 'linear',
            dtick = 1
        )
    )

    clrs = ['red' if (y > 80) else '#5296dd' for y in df_hours.contribution_count] 

    fig2.update_traces(marker_color=clrs,
                      marker_line_width=1.5, opacity=1, textposition='auto').update_layout()
    fig2.show()


peack_days('2018-07-03')

How many users contributed on the peak day (2018-07-03)
98
Years

hours


peack_days('2018-07-22')

How many users contributed on the peak day (2018-07-22)
50
Years

hours


peack_days('2018-07-24')

How many users contributed on the peak day (2018-07-24)
79
Years

hours


peack_days('2018-12-19')

How many users contributed on the peak day (2018-12-19)
96
Years

hours


peack_days('2018-12-20')

How many users contributed on the peak day (2018-12-20)
94
Years

hours

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls	urls_count
0	t3_7zgadd	/r/SexyWomanOfTheDay/comments/7zgadd/amber_hea...	Amber Heard: easy on the eyes	NaN	r/SexyWomanOfTheDay	Rednaxela117	2018-02-22 16:47:57	Positive	Positive	73.0	NaN	submission	6	amber_heard_easy_on_the_eyes	6	[]	0
1	t3_7zgcdi	/r/SexyWomanOfTheDay/comments/7zgcdi/amber_hea...	Amber Heard: interesting swimwear	NaN	r/SexyWomanOfTheDay	Rednaxela117	2018-02-22 16:54:44	Positive	Positive	72.0	NaN	submission	4	amber_heard_interesting_swimwear	4	[]	0
2	t1_dunqfh1	/r/SexyWomanOfTheDay/comments/7zg975/amber_hea...	Apparently she's the most beautiful woman in t...	t3_7zg975	r/SexyWomanOfTheDay	Rednaxela117	2018-02-22 16:57:06	Positive	Positive	2.0	submission	comment	13	amber_heard_is_todays_sexy_woman_of_the_day	9	['https://www.maxim.com']	1
3	t3_7zgdf3	/r/SexyWomanOfTheDay/comments/7zgdf3/amber_hea...	Amber Heard: as Mera	NaN	r/SexyWomanOfTheDay	Rednaxela117	2018-02-22 16:58:11	Neutral	Neutral	114.0	NaN	submission	4	amber_heard_as_mera	4	[]	0
4	t3_7zgh46	/r/SexyWomanOfTheDay/comments/7zgh46/amber_hea...	Amber Heard: lounging around	NaN	r/SexyWomanOfTheDay	Rednaxela117	2018-02-22 17:11:13	Neutral	Neutral	63.0	NaN	submission	4	amber_heard_lounging_around	4	[]	0

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
107	t3_7rq9ap	/r/dirtypenpals/comments/7rq9ap/22f4a_anyone_with_a_fucked_up_mind_have_a_crush/	22f4a - Anyone with a fucked up mind have a crush on Amber Heard, Lexi Belle, Lauren Cohan, Shay Mitchell?	NaN	r/dirtypenpals	-banned-	2018-01-20 13:02:08	Negative	Negative	1.0	NaN	submission	20	22f4a_anyone_with_a_fucked_up_mind_have_a_crush	10	[]
265	t3_7wiqf8	/r/JerkOffToCelebs/comments/7wiqf8/amber_heard_deserves_a_good_rough_fuck/	Amber Heard deserves a good rough fuck.	NaN	r/JerkOffToCelebs	arsenalmat	2018-02-10 02:40:27	Positive	Neutral	20.0	NaN	submission	7	amber_heard_deserves_a_good_rough_fuck	7	[]
433	t3_82lvbs	/r/gentlemanboners/comments/82lvbs/amber_heard_left_fuck_it_id_still_tap_her/	Amber heard (left): fuck it I’d still tap her!$!!	NaN	r/gentlemanboners	-banned-	2018-03-07 05:21:31	Negative	Negative	0.0	NaN	submission	9	amber_heard_left_fuck_it_id_still_tap_her	9	[]
1055	t3_8cflf9	/r/JerkOffToCelebs/comments/8cflf9/amber_heard_is_ready_to_get_fucked_and_swallow_cum/	Amber Heard is ready to get fucked and swallow cum	NaN	r/JerkOffToCelebs	-banned-	2018-04-15 14:51:48	Negative	Neutral	21.0	NaN	submission	10	amber_heard_is_ready_to_get_fucked_and_swallow_cum	10	[]
1769	t3_8p6csr	/r/JerkOffToCelebs/comments/8p6csr/amber_heard_is_so_fucking_hot/	Amber Heard is so fucking hot	NaN	r/JerkOffToCelebs	arsenalmat	2018-06-07 01:42:34	Positive	Neutral	34.0	NaN	submission	6	amber_heard_is_so_fucking_hot	6	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
450	t3_82xah3	/r/WatchItForThePlot/comments/82xah3/amber_heard_the_informers_2008/	Amber Heard - The Informers (2008)	NaN	r/WatchItForThePlot	-banned-	2018-03-08 12:58:59	Neutral	Neutral	264.0	NaN	submission	6	amber_heard_the_informers_2008	5	[]
1264	t3_8hx1bz	/r/WatchItForThePlot/comments/8hx1bz/amber_heard_the_informers/	Amber Heard - The Informers	NaN	r/WatchItForThePlot	-banned-	2018-05-08 14:18:26	Neutral	Neutral	3749.0	NaN	submission	5	amber_heard_the_informers	4	[]
1282	t3_8hzcyg	/r/CelebsGW/comments/8hzcyg/amber_heard_the_informers/	Amber Heard - The Informers	NaN	r/CelebsGW	CelebsGW	2018-05-08 19:06:20	Neutral	Neutral	91.0	NaN	submission	5	amber_heard_the_informers	4	[]
2225	t3_8vslyy	/r/WatchItForThePlot/comments/8vslyy/amber_heard_the_informers_2008/	Amber Heard - The Informers (2008)	NaN	r/WatchItForThePlot	-banned-	2018-07-03 14:26:24	Neutral	Neutral	279.0	NaN	submission	6	amber_heard_the_informers_2008	5	[]
2825	t3_91crfc	/r/WatchItForThePlot/comments/91crfc/amber_heard_the_informers/	Amber Heard - The Informers	NaN	r/WatchItForThePlot	Ezio9619	2018-07-24 00:51:55	Neutral	Neutral	459.0	NaN	submission	5	amber_heard_the_informers	4	[]

	index	author
0	-banned-	1666
1	Night_Chicken	135
2	AutoModerator	50
3	emilyguy	38
4	AutoNewsAdmin	35
5	ccrraapp	34
6	Rednaxela117	33
7	AutoNewspaperAdmin	33
8	ZorakLocust	27
9	tenchineuro	22

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
0	t1_dslw4u6	/r/Celebs/comments/7pysyp/amber_heard/dslw4u6/	What? What did she hear?	t3_7pysyp	r/Celebs	Night_Chicken	2018-01-13 04:48:41	Neutral	Neutral	-6.0	submission	comment	5	amber_heard	2	[]
1	t1_dvufzj9	/r/Celebs/comments/844rw7/amber_heard/dvufzj9/	What? Thank you!	t1_dvn0nfy	r/Celebs	Night_Chicken	2018-03-17 13:22:00	Neutral	Positive	1.0	comment	comment	3	amber_heard	2	[]
2	t1_dvug20l	/r/Celebs/comments/81y6lx/amber_heard/dvug20l/	What? What did she hear?	t3_81y6lx	r/Celebs	Night_Chicken	2018-03-17 13:23:50	Neutral	Neutral	1.0	submission	comment	5	amber_heard	2	[]
3	t1_e3uftxn	/r/Celebs/comments/94w2gm/amber_heard/e3uftxn/	What? What did she hear?	t3_94w2gm	r/Celebs	Night_Chicken	2018-08-08 20:07:11	Neutral	Neutral	1.0	submission	comment	5	amber_heard	2	[]
4	t1_e3ufvla	/r/Celebs/comments/94tmn2/amber_heard/e3ufvla/	What? What did she hear?	t3_94tmn2	r/Celebs	Night_Chicken	2018-08-08 20:07:50	Neutral	Neutral	1.0	submission	comment	5	amber_heard	2	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
0	t3_82wa5c	/r/gentlemanboners/comments/82wa5c/amber_heard/	Amber Heard	NaN	r/gentlemanboners	emilyguy	2018-03-08 09:30:44	Neutral	Neutral	3814.0	NaN	submission	2	amber_heard	2	[]
1	t3_842r8g	/r/gentlemanboners/comments/842r8g/amber_heard/	Amber Heard	NaN	r/gentlemanboners	emilyguy	2018-03-13 09:09:02	Neutral	Neutral	442.0	NaN	submission	2	amber_heard	2	[]
2	t3_85j0o6	/r/gentlemanboners/comments/85j0o6/amber_heard/	Amber Heard	NaN	r/gentlemanboners	emilyguy	2018-03-19 12:18:13	Neutral	Neutral	595.0	NaN	submission	2	amber_heard	2	[]
3	t3_8694b7	/r/WatchItForThePlot/comments/8694b7/amber_hea...	Amber Heard has an amazing body	NaN	r/WatchItForThePlot	emilyguy	2018-03-22 05:16:10	Positive	Positive	3165.0	NaN	submission	6	amber_heard_has_an_amazing_body	6	[]
4	t3_8694cl	/r/celebsnaked/comments/8694cl/amber_heard/	Amber Heard	NaN	r/celebsnaked	emilyguy	2018-03-22 05:16:21	Neutral	Neutral	316.0	NaN	submission	2	amber_heard	2	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
0	t1_e0pfa56	/r/DCEUboners/comments/8r6fjx/amber_heard/e0pf...	Oh wow!	t3_8r6fjx	r/DCEUboners	ccrraapp	2018-06-15 07:40:04	Positive	Positive	2.0	submission	comment	2	amber_heard	2	[]
1	t1_e0pfbb5	/r/DCEUboners/comments/8r6bq6/nicole_kidman_am...	Nicole Kidman looks stunning. At this point on...	t3_8r6bq6	r/DCEUboners	ccrraapp	2018-06-15 07:41:12	Positive	Positive	1.0	submission	comment	15	nicole_kidman_amber_heard	4	[]
2	t1_e0x3uz0	/r/DCEUboners/comments/8s4e6n/amber_heard/e0x3...	Her dress is so perfectly wrapping her.	t3_8s4e6n	r/DCEUboners	ccrraapp	2018-06-19 09:09:46	Positive	Positive	1.0	submission	comment	7	amber_heard	2	[]
3	t1_e2pq776	/r/DCEUboners/comments/90dxcj/amber_heard/e2pq...	Did she lose a lot of weight for this role? T...	t3_90dxcj	r/DCEUboners	ccrraapp	2018-07-20 08:23:49	Positive	Neutral	2.0	submission	comment	15	amber_heard	2	[]
4	t1_e2psowf	/r/DCEUboners/comments/90dxcj/amber_heard/e2ps...	Yes but she looks slim than before.	t1_e2prpct	r/DCEUboners	ccrraapp	2018-07-20 09:51:55	Neutral	Positive	2.0	comment	comment	7	amber_heard	2	[]

	created_at	n_contributions
17	2018-12-06	3
4	2018-09-10	3
0	2018-06-15	2
9	2018-11-02	2
18	2018-12-09	2
15	2018-11-28	2
14	2018-11-27	2
13	2018-11-20	2
11	2018-11-13	2
10	2018-11-08	2

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
0	t3_8a394f	/r/AutoNewspaper/comments/8a394f/entertainment...	[Entertainment] - Amber Heard says meeting Syr...	NaN	r/AutoNewspaper	AutoNewspaperAdmin	2018-04-05 20:31:39	Neutral	Neutral	1.0	NaN	submission	14	entertainment_amber_heard_says_meeting_syria	6	[]
1	t3_8a3i6o	/r/AutoNewspaper/comments/8a3i6o/entertainment...	[Entertainment] - Amber Heard says meeting Syr...	NaN	r/AutoNewspaper	AutoNewspaperAdmin	2018-04-05 21:01:37	Neutral	Neutral	1.0	NaN	submission	14	entertainment_amber_heard_says_meeting_syria	6	[]
2	t3_8jp0vd	/r/AutoNewspaper/comments/8jp0vd/lifestyle_in_...	[Lifestyle] - In Cannes, Amber Heard chats abo...	NaN	r/AutoNewspaper	AutoNewspaperAdmin	2018-05-15 20:47:12	Neutral	Neutral	1.0	NaN	submission	16	lifestyle_in_cannes_amber_heard_chats_about_big	8	[]
3	t3_8ttimo	/r/AutoNewspaper/comments/8ttimo/national_ambe...	[National] - Amber Heard, other celebs travel ...	NaN	r/AutoNewspaper	AutoNewspaperAdmin	2018-06-25 19:46:01	Negative	Negative	1.0	NaN	submission	17	national_amber_heard_other_celebs_travel_to_texas	8	[]
4	t3_8vur4r	/r/AutoNewspaper/comments/8vur4r/video_amber_h...	[Video] - Amber Heard upsets fans with offensi...	NaN	r/AutoNewspaper	AutoNewspaperAdmin	2018-07-03 18:48:38	Neutral	Negative	1.0	NaN	submission	11	video_amber_heard_upsets_fans_with_offensive	7	[]

	created_at	n_contributions
0	2018-04-05	2
1	2018-05-15	1
2	2018-06-25	1
3	2018-07-03	3
4	2018-09-14	1
5	2018-10-03	2
6	2018-10-10	1
7	2018-10-22	4
8	2018-10-25	5
9	2018-10-29	1
10	2018-11-13	1
11	2018-11-26	1
12	2018-11-27	2
13	2018-12-03	1
14	2018-12-05	2
15	2018-12-06	2
16	2018-12-12	1
17	2018-12-19	2

	created_at	n_contributions
0	2018-02-22	8
3	2018-05-01	8
1	2018-02-23	2
2	2018-03-28	2
4	2018-05-02	2
13	2018-12-29	2
5	2018-05-03	1
6	2018-05-25	1
7	2018-07-10	1
8	2018-07-22	1

	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year
1520	FlexOutlaw	True	False	False	False	5217.0	154864.0	2010-09-25 18:36:46	others	others
4432	AutoModerator	True	True	True	False	1000.0	1000.0	2012-01-05 05:24:28	others	others

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
5	t1_ds0ylr9	/r/JerkOffToCelebs/comments/7nbhsf/amber_heard...	Tits look primed to get fucked, though yeah, I...	t3_7nbhsf	r/JerkOffToCelebs	-banned-	2018-01-01 04:44:31	Negative	Neutral	2.0	submission	comment	14	amber_heard_would_be_such_a_dirty_slut_in_bed	10	[]
6	t1_ds1ghu7	/r/elonmusk/comments/7n76bc/amber_heard_and_el...	He's asking for it - he's living a soap opera,...	t1_ds0339s	r/elonmusk	-banned-	2018-01-01 16:38:18	Negative	Neutral	2.0	comment	comment	81	amber_heard_and_elon_musk_spotted_vacationing_in	8	[]
13	t1_ds2hdi8	/r/elonmusk/comments/7mbv6q/amber_heard_and_el...	She's a fucking spider.	t3_7mbv6q	r/elonmusk	BoracayBatCave	2018-01-02 05:29:24	Negative	Neutral	2.0	submission	comment	4	amber_heard_and_elon_musk_are_reportedly_back	8	[]
21	t1_ds4unfi	/r/gentlemanboners/comments/7nolyx/amber_heard...	Damn Elon you fucked up	t3_7nolyx	r/gentlemanboners	-banned-	2018-01-03 17:01:17	Negative	Negative	1.0	submission	comment	5	amber_heard	2	[]
35	t3_7ohkn2	/r/u_NeverOwnedAnyone/comments/7ohkn2/while_el...	While Elin Musk is riding high on his free pat...	NaN	u/NeverOwnedAnyone	-banned-	2018-01-06 06:14:59	Positive	Positive	1.0	NaN	submission	29	u_NeverOwnedAnyone	2	[]

	author	n_contributions
0	-banned-	122
184	ZorakLocust	16
31	Chronos2016	15
115	NaveHarder	14
14	AutoModerator	12
93	KAIZOKUGARI23	12
270	serjon_arryn	10
78	Hydrostorm9	9
22	BatmanNewsChris	8
34	CliffordMoreau	8

	author	n_contributions
0	-banned-	30
91	tenchineuro	22
67	j3utton	13
37	Rogdozz	12
87	scyth3s	10
20	LateNightTestPattern	8
15	GreyFox860	7
52	_pseudodragon	6
82	purpleblossom	5
57	chambertlo	5

	created_at	n_contributions
11	2018-12-28	3
9	2018-09-27	2
0	2018-01-20	1
1	2018-02-10	1
2	2018-03-07	1
3	2018-04-15	1
4	2018-06-07	1
5	2018-06-16	1
6	2018-06-28	1
7	2018-07-08	1
8	2018-07-22	1
10	2018-12-25	1

	created_at	n_contributions
96	2018-12-19	14
97	2018-12-20	10
24	2018-05-02	5
61	2018-08-15	5
103	2018-12-28	5
59	2018-08-11	5
35	2018-06-06	5
88	2018-11-19	4
27	2018-05-08	4
58	2018-08-10	4

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
5439	t3_a37wmy	/r/celebJObuds/comments/a37wmy/need_a_bud_to_r...	Need a bud to RP as Amber Heard for me	NaN	r/celebJObuds	Count_Fapula1	2018-12-05 02:40:39	Neutral	Neutral	19.0	NaN	submission	10	need_a_bud_to_rp_as_amber_heard_for_me	10	[]
5440	t3_a381td	/r/JerkOffToCelebs/comments/a381td/what_id_giv...	What I'd give to have Amber Heard worshipping ...	NaN	r/JerkOffToCelebs	Count_Fapula1	2018-12-05 02:57:38	Neutral	Neutral	52.0	NaN	submission	10	what_id_give_to_have_amber_heard_worshipping_my	9	[]
5463	t3_a3fhl3	/r/celebJObuds/comments/a3fhl3/still_looking_f...	Still looking for a bud to RP as Amber Heard a...	NaN	r/celebJObuds	Count_Fapula1	2018-12-05 19:14:12	Neutral	Positive	5.0	NaN	submission	14	still_looking_for_a_bud_to_rp_as_amber_heard_and	11	[]
5465	t1_eb5q31k	/r/JerkOffToCelebs/comments/a381td/what_id_giv...	Right? She looks like the type that just *love...	t1_eb5pyp4	r/JerkOffToCelebs	Count_Fapula1	2018-12-05 19:15:59	Positive	Neutral	4.0	comment	comment	11	what_id_give_to_have_amber_heard_worshipping_my	9	[]
5796	t1_ebqzlr1	/r/JerkOffToCelebs/comments/a5twvl/amber_heard...	Right? Sometimes I browse r/Amber_Heard and I'...	t3_a5twvl	r/JerkOffToCelebs	Count_Fapula1	2018-12-14 04:57:01	Positive	Neutral	3.0	submission	comment	11	amber_heard_is_so_damn_hot	6	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
104	t1_dsygyxg	/r/JerkOffToCelebs/comments/7roj2h/amber_heard...	She would be a really fun fuck...	t3_7roj2h	r/JerkOffToCelebs	DariJC	2018-01-20 05:38:22	Negative	Positive	13.0	submission	comment	7	amber_heardtight_and_wet	4	[]
3888	t1_e4xbxip	/r/JerkOffToCelebs/comments/9aqp5j/amber_heard...	Giving her a rough fuck would be incredibly fu...	t3_9aqp5j	r/JerkOffToCelebs	DariJC	2018-08-27 17:03:46	Negative	Neutral	2.0	submission	comment	35	amber_heard	2	[]
4011	t1_e52f18p	/r/JerkOffToCelebs/comments/9be7uy/amber_heard...	Needa tear off those panties and take her righ...	t3_9be7uy	r/JerkOffToCelebs	DariJC	2018-08-29 23:42:09	Positive	Neutral	2.0	submission	comment	10	amber_heard_is_freshening_up_before_the_next_r...	9	[]
4145	t1_e6rez2z	/r/celebJObuds/comments/9jh06j/id_fuck_the_shi...	Lightly push her forward and lift that dress u...	t3_9jh06j	r/celebJObuds	DariJC	2018-09-27 22:06:24	Positive	Neutral	3.0	submission	comment	12	id_fuck_the_shit_out_of_amber_heard_in_that	10	[]
4148	t1_e6rf3qo	/r/celebJObuds/comments/9jh06j/id_fuck_the_shi...	She def looks like she’s into the rough stuff	t1_e6rf0x6	r/celebJObuds	DariJC	2018-09-27 22:08:17	Negative	Positive	5.0	comment	comment	9	id_fuck_the_shit_out_of_amber_heard_in_that	10	[]

	created_at	n_contributions
17	2018-08-29	103
22	2018-09-24	4
28	2018-10-14	4
6	2018-03-13	2
15	2018-08-08	2

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
10	t3_7nkbt3	/r/gentlemanboners/comments/7nkbt3/amber_heard/	Amber Heard	NaN	r/gentlemanboners	ZadocPaet	2018-01-02 05:07:43	Neutral	Neutral	5.0	NaN	submission	2	amber_heard	2	[]
11	t3_7nkbua	/r/DCEUboners/comments/7nkbua/amber_heard/	Amber Heard	NaN	r/DCEUboners	ZadocPaet	2018-01-02 05:07:55	Neutral	Neutral	45.0	NaN	submission	2	amber_heard	2	[]
16	t3_7nolyx	/r/gentlemanboners/comments/7nolyx/amber_heard/	Amber Heard	NaN	r/gentlemanboners	-banned-	2018-01-02 19:17:12	Neutral	Neutral	330.0	NaN	submission	2	amber_heard	2	[]
20	t3_7nuh77	/r/gentlemanboners/comments/7nuh77/lovely_duo_amber_heard_jessica_alba/	Lovely Duo. Amber Heard & Jessica Alba	NaN	r/gentlemanboners	-banned-	2018-01-03 12:58:23	Positive	Positive	570.0	NaN	submission	7	lovely_duo_amber_heard_jessica_alba	6	[]
24	t3_7nxwrq	/r/gentlemanboners/comments/7nxwrq/amber_heard/	Amber Heard	NaN	r/gentlemanboners	-banned-	2018-01-03 21:34:14	Neutral	Neutral	2.0	NaN	submission	2	amber_heard	2	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
1726	t1_e0830mk	/r/movies/comments/8ozhus/first_poster_london_fields_amber_heard_billy_bob/e0830mk/	Fuck Amber Heard, she's a lier and a gold digger.	t3_8ozhus	r/movies	-banned-	2018-06-06 18:59:57	Negative	Negative	-6.0	submission	comment	10	first_poster_london_fields_amber_heard_billy_bob	8	[]
1776	t1_e091jem	/r/movies/comments/8p6bah/london_fields_official_trailer_2018_amber_heard/e091jem/	Fuck Amber Heard!	t3_8p6bah	r/movies	-banned-	2018-06-07 04:15:22	Negative	Negative	1.0	submission	comment	3	london_fields_official_trailer_2018_amber_heard	7	[]
4287	t1_e79113k	/r/goddesses/comments/9ljspc/amber_heard/e79113k/	Fuck Amber Heard	t3_9ljspc	r/goddesses	-banned-	2018-10-06 01:25:58	Negative	Negative	3.0	submission	comment	3	amber_heard	2	[]
4293	t1_e7dgj5g	/r/goddesses/comments/9maij0/amber_heard/e7dgj5g/	Fuck Amber Heard	t3_9maij0	r/goddesses	-banned-	2018-10-08 03:33:40	Negative	Negative	0.0	submission	comment	3	amber_heard	2	[]

	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year
29	RyanSmith	True	True	False	False	263666.0	5989322.0	2006-08-07 14:18:44	others	others
230	soundsoul	True	True	False	False	4156.0	238286.0	2008-01-04 02:52:49	others	others
1488	littlemisfit	True	True	False	False	9523.0	602311.0	2010-09-17 22:11:14	others	others
1520	FlexOutlaw	True	False	False	False	5217.0	154864.0	2010-09-25 18:36:46	others	others
2974	jarakacha	True	True	False	False	142.0	153129.0	2011-07-20 21:10:48	others	others
...	...	...	...	...	...	...	...	...	...	...
66206	jasontheblogger2018	True	True	True	True	NaN	NaN	NaT	banned	banned
66258	Snappleman87	True	True	True	True	NaN	NaN	NaT	banned	banned
66342	88MPH1	True	True	True	True	NaN	NaN	NaT	banned	banned
68878	armpit-lover	True	True	True	True	NaN	NaN	NaT	banned	banned
70281	HellsJuggernaut	True	True	True	True	NaN	NaN	NaT	banned	banned

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
190	t3_7vr5ia	/r/DC_Cinematic/comments/7vr5ia/discussion_amber_heard_playing_mera_rubs_me_the/	DISCUSSION: Amber Heard playing Mera rubs me the wrong way	NaN	r/DC_Cinematic	ZorakLocust	2018-02-06 22:16:01	Negative	Neutral	0.0	NaN	submission	10	DC_Cinematic	2	[]
479	t3_82z9f0	/r/DC_Cinematic/comments/82z9f0/rumor_aquaman_press_tour_with_amber_heard_and/	RUMOR: Aquaman press tour with Amber Heard and Jason Momoa is starting SOON in Europe!	NaN	r/DC_Cinematic	Sabya2kMukherjee	2018-03-08 17:30:32	Neutral	Neutral	125.0	NaN	submission	15	DC_Cinematic	2	[]
859	t3_89jugi	/r/DC_Cinematic/comments/89jugi/other_amber_heard_visits_children_in_syrian/	OTHER: Amber Heard visits children in Syrian refugee camp of Zaatari	NaN	r/DC_Cinematic	-banned-	2018-04-03 23:21:48	Negative	Neutral	1.0	NaN	submission	11	DC_Cinematic	2	[]
887	t3_8bbokl	/r/DC_Cinematic/comments/8bbokl/news_heroes_onscreen_and_off_amber_heard_donates/	NEWS: Heroes onscreen and off: Amber Heard donates to children's hospital	NaN	r/DC_Cinematic	-banned-	2018-04-10 21:40:38	Neutral	Positive	81.0	NaN	submission	11	DC_Cinematic	2	[]
925	t3_8c2e0d	/r/DC_Cinematic/comments/8c2e0d/social_media_amber_heard_aquafied_yet_again/	Social Media: Amber Heard: Aquafied yet again	NaN	r/DC_Cinematic	Mohamed_Todd	2018-04-13 20:33:56	Positive	Neutral	163.0	NaN	submission	7	DC_Cinematic	2	[]

	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year
2981	indian22	True	False	False	False	6300.0	69586.0	2011-07-21 10:57:48	others	others
3846	-banned-	True	False	False	False	77234.0	82.0	2011-11-06 21:08:20	others	others
10847	boumtjeboo	True	False	False	False	89482.0	116498.0	2013-08-13 21:48:05	others	others
11664	GlowInThe	True	False	False	False	27992.0	87943.0	2013-10-29 06:50:39	others	others
13934	Rugby11	True	True	False	False	2397.0	114562.0	2014-05-19 17:41:26	others	others
19407	Richardrumeo	True	False	False	False	14716.0	40662.0	2015-08-18 18:01:48	others	others
20990	Mohamed_Todd	True	False	False	False	7017.0	28735.0	2016-01-01 21:31:20	others	others
21016	BeenTryin	True	False	False	False	29570.0	313003.0	2016-01-03 17:29:07	others	others
21386	ZorakLocust	True	False	False	False	29289.0	10356.0	2016-01-30 19:10:14	others	others
24215	Sabya2kMukherjee	True	False	False	False	55889.0	37522.0	2016-07-31 18:15:40	others	others
25133	GamesFictionFan	True	False	False	False	16722.0	9220.0	2016-09-22 21:50:48	others	others
27036	RobustBender	True	False	False	False	54570.0	3062.0	2017-01-06 03:14:00	others	others
30721	AldebaranTauro	True	False	False	False	4626.0	376091.0	2017-07-19 01:17:55	others	others
32853	atulsachdeva	True	True	False	False	17528.0	4660.0	2017-11-12 23:06:55	others	others
34463	Kal_sai	True	True	False	False	34327.0	41936.0	2018-01-27 13:08:45	others	2018
37226	Staticrealms	True	False	False	False	261.0	4027.0	2018-05-29 14:57:52	others	2018
38850	kyotomafia1997	True	False	False	False	1642.0	5462.0	2018-08-01 04:30:18	others	2018
41777	ThatBrandingGuy	True	False	False	False	7689.0	29968.0	2018-11-11 19:17:29	others	2018
66041	Hakim36	True	True	True	True	NaN	NaN	NaT	banned	banned

Case Study: Reddit Social Network Analysis Against Influence Operation¶

Table of Contents¶

Introduction¶

Exploratory Data Analysis¶

Reddit Contributions (Comments / Submissions)¶

The # of submissions VS the # of comments¶

Most contributed user¶

Further Investigate The Most Commented Users In 2018¶

AutoModerator¶

Night_Chicken¶

emilyguy¶

ccrraapp¶

AutoNewspaperAdmin¶

Rednaxela117¶

Check wether the users with the most contributions are mod, gold or having a verified email¶

Investigate the text column¶

Investigate The Most Negative key Words Used (from the wordcloud map)¶

Lets first check for the users using the word 'f*ck'¶

Count_Fapula1¶

DariJC¶

Check for the repeated text¶

What? What did she hear?¶

[Aquaman] Amber Heard¶

Amber Heard - The Informers¶

Amber Heard¶

Amber Heard¶

Further check Lecrapface contributions on 2018¶

Check the number of text words

Reddit Comments¶

The number of parent comments on submissions¶

Reddit Submission Data¶

Investigating the Submission Text (Submissions with the most comments and replies)

amber_heard¶

DC_Cinematic¶

amber_heard_received_death_threats_and_was¶

Invesigating the sumbissions with most comments (Top Level Comments)

Check the top 5 submissions in 2018¶

Define a function to create the mask and check if a submission text is part of the top 5 submissions¶

Check the authors of parent comments¶

Invesigating authors with the most submissions¶

Let us first Investigate authors with the most submissions¶

Check wether the users with the most submissions are mod, gold or having a verified email¶

Submission URLS¶

Check the number of submission text words

Subredits¶

Most used Subreddits¶

Merged Users Data with Comments & Submissions Data¶

Difference in time between creating the account and posting¶

Posting Duration After Account Creation¶

THE SAME MONTH:¶

THE SAME WEEK¶

THE SAME DAY¶

Estimation of Number of User Accounts Created in each year / having contributions in 2021¶

Peaks¶

Contributions count over Months in 2018¶

Contributions count over Days of month in 2018¶

In Which DayOfWeek users created more?¶

check for the hour the contributions were made (2018)¶

Which dates has the highest contrbitions for users?¶

How many users contributed on the peak day (Jul 03,2018)¶

How many users contributed on the peak day (Jul 22,2018)¶

How many users contributed on the peak day (Jul 24,2018)¶

How many users contributed on the peak day (Dec 19,2018)¶

How many users contributed on the peak day (Dec 20,2018)¶

Conclusions¶

Investigating the Submission Text
(Submissions with the most comments and replies)

Invesigating the sumbissions with most comments
(Top Level Comments)

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls	urls_count
2	t1_ds0x0jx	/r/elonmusk/comments/7n76bc/amber_heard_and_el...	Here's a sneak peek of /r/MGTOW using the [top...	t1_ds0x0d9	r/elonmusk	sneakpeekbot	2018-01-01 03:55:47	Negative	Negative	3.0	comment	comment	64	amber_heard_and_elon_musk_spotted_vacationing_in	8	['https://np.reddit.com', 'https://i.imgur.com...	9
14	t1_ds2okme	/r/elonmusk/comments/7n76bc/amber_heard_and_el...	Apparently common sense has escaped Elon Musk ...	t1_ds1q00a	r/elonmusk	-banned-	2018-01-02 10:01:07	Negative	Neutral	3.0	comment	comment	62	amber_heard_and_elon_musk_spotted_vacationing_in	8	['http://docs.cpuc.ca.gov']	1

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
3144	t1_dyc2my4	/r/celebJObuds/comments/8gdrmz/any_buds_want_t...	I'd fuck the crazy right out of her	t3_8gdrmz	r/celebJObuds	2018-05-02 17:32:46	Negative	Negative	2.0	submission	...	False	False	False	1022.0	2649.0	2018-04-04 04:06:28	others	2018	28 days 13:26:18	28.0
3262	t1_dytrknx	/r/premed/comments/8iiisc/scrolling_through_my...	That makes me so mad! Damn you, Caduceus!	t1_dys31hq	r/premed	2018-05-11 19:17:10	Negative	Negative	1.0	comment	...	False	False	False	8516.0	2057.0	2018-04-27 18:57:47	others	2018	14 days 00:19:23	14.0
4394	t1_e2ulws0	/r/movies/comments/910nqx/just_a_friendly_remi...	OP really picked the wrong example to prove th...	t1_e2ujdpj	r/movies	2018-07-22 20:58:35	Negative	Negative	24.0	comment	...	False	False	False	34616.0	42684.0	2018-07-15 17:03:43	unverified	2018	7 days 03:54:52	7.0
4545	t1_e2zfi6i	/r/JerkOffToCelebs/comments/91mf8f/which_redhe...	ScarJo i mean dat ass	t3_91mf8f	r/JerkOffToCelebs	2018-07-25 02:22:00	Negative	Negative	11.0	submission	...	False	False	False	309.0	1.0	2018-07-21 15:43:24	unverified	2018	3 days 10:38:36	3.0
5474	t1_e8ffubo	/r/CelebAssPussyMouth/comments/9r48bq/another_...	G and K all day long.\n\nIt was a difficult ch...	t3_9r48bq	r/CelebAssPussyMouth	2018-10-25 14:56:22	Negative	Negative	4.0	submission	...	False	False	False	684.0	600.0	2018-10-01 12:42:45	others	2018	24 days 02:13:37	24.0

	created_at	contribution_count
173	2018-07-03	190
192	2018-07-22	137
194	2018-07-24	128
339	2018-12-19	244
340	2018-12-20	150